The global architecture of our model follows the one used by [1]}. The architecture of the PUnet is made of two parts. On the one hand, there is a classical U-Net [2]} architecture, whose goal is to create realistic segmentation maps from the input galaxy images. This constitutes the deterministic part of the model. On the other hand, we have an encoder that compresses the images into a Gaussian latent space. This latent space is sampled and concatenated to the output of the U-Net before a final convolution layer followed by a softmax activation to produce a non-deterministic output. The output is then thresholded to produce the desired segmentation map with overlapping regions.
